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Deal With Advanced Methods of Data Analsis and Will Cover Both Statistical and Machine Learning Tools For The Purpose of Analyzing Data, Visualization, Classification and Prediction. Specific Topics# Linear Regression, Classification, Pac Learning, Support Vector Machine, Resampling, Model Selection and Regularization, Decision Trees and Regression. Cluster Analysis. Learning Outcomes# Understanding The Theory of The Different Methods and Having The Ability to Apply It On Real Data.

Faculty: Data and Decision Sciences
|Undergraduate Studies |Graduate Studies

Pre-required courses

(94423 - Introduction to Statistics and 234117 - Introduction to Computer Science H) or (94423 - Introduction to Statistics and 234221 - Introduction to Computer Science N) or (94423 - Introduction to Statistics and 234111 - Introduction to Computer Science) or (94424 - Statistics 1 and 234117 - Introduction to Computer Science H) or (94424 - Statistics 1 and 234221 - Introduction to Computer Science N) or (94424 - Statistics 1 and 234111 - Introduction to Computer Science)

• An introduction to statistical learning : with applications in R
• An introduction to statistical learning : with applications in R - James, Gareth
• An Introduction to Statistical Learning [electronic resource] : with Applications in R - James, Gareth.
• The elements of statistical learning : data mining, inference, and prediction - Hastie, Trevor.
• The Elements of Statistical Learning [electronic resource] : Data Mining, Inference, and Prediction, Second Edition - Hastie, Trevor.
• Understanding machine learning : from theory to algorithms - Shalev-Shwartz, Shai